Automated Adaptive Data Analysis Using Dynamic Data Quality Assessment

ABSTRACT

In general, embodiments of the present invention provide systems, methods and computer readable media for automated dynamic data quality assessment. One aspect of the subject matter described in this specification includes the actions of receiving a data quality job including a new data sample; and, if the new data sample is determined to be added to a reservoir of data samples, sending a quality verification request to an oracle; receiving a new data sample quality estimate from the oracle; and adding the new data sample and estimate to the reservoir. A second aspect of the subject matter includes the actions of receiving, from a predictive model, a judgment associated with a new data sample; analyzing the new data sample based in part on the judgment to determine whether to send a new data sample quality verification request to an oracle; and, if a new data sample quality estimate is received from the oracle, determining whether to add the new data sample and the judgment to the reservoir.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/427,908, filed Feb. 8, 2017, titled “AUTOMATED ADAPTIVE DATA ANALYSISUSING DYNAMIC DATA QUALITY ASSESSMENT,” which is a continuation of U.S.application Ser. No. 14/088,247, filed Nov. 22, 2013, titled “AUTOMATEDADAPTIVE DATA ANALYSIS USING DYNAMIC DATA QUALITY ASSESSMENT,” now U.S.Pat. No. 9,600,776, the contents of which are incorporated herein byreference in their entirety.

This application is related to the following commonly assigned patent:Attorney Docket Number 058407/438965, U.S. application Ser. No.14/088,248, filed Nov. 22, 2013, now U.S. Pat. No. 9,390,112, titled“Automated Dynamic Data Quality Assessment,” listing Mark Daly, ShawnJeffery, Matthew DeLand, Nick Pendar, Andrew James, and David Johnstonas inventors.

FIELD

Embodiments of the invention relate, generally, to automated dynamicdata quality assessment.

BACKGROUND

A system that automatically identifies new businesses based on datasampled from a data stream representing data collected from a variety ofonline sources (e.g., websites, blogs, and social media) is an exampleof a system that processes dynamic data. Analysis of such dynamic datatypically is based on data-driven statistical models that depend onconsistent data quality, yet dynamic data is inherently inconsistent inits quality.

Current methods for dynamic data quality assessment exhibit a pluralityof problems that make current systems insufficient, ineffective and/orthe like. Through applied effort, ingenuity, and innovation, solutionsto improve such methods have been realized and are described inconnection with embodiments of the present invention.

SUMMARY

In general, embodiments of the present invention provide herein systems,methods and computer readable media for automated dynamic data qualityassessment.

In general, one aspect of the subject matter described in thisspecification can be embodied in systems, methods, and computer programproducts that include the actions of receiving a data quality job, thedata quality job including configuration data and a new data samplehaving a particular data type, wherein the configuration data comprisesan oracle identifier; determining whether to add the new data sample toa reservoir of data samples identified based at least in part on theparticular data type; and, in an instance in which the new data sampleis to be added to the reservoir of data samples, performing actionsincluding sending, to an oracle selected based on the oracle identifier,a quality verification request including the new data sample; receivinga data quality estimate associated with the new data sample from theoracle in response to the quality verification request; and adding thenew data sample and the associated data quality estimate to thereservoir of data samples in response to receiving the data qualityestimate.

These and other embodiments can optionally include one or more of thefollowing features. The actions may further include updating thereservoir summary statistics. Updating the reservoir summary statisticsmay include calculating an overall data quality estimate for thereservoir using data quality estimates respectively associated with eachof the data samples; and calculating a statistical variance for the datasamples. Updating the reservoir summary statistics may further includelogging the updated reservoir summary statistics in persistent storage.The actions may further include receiving corpus summary statisticscalculated for a corpus of previously collected data samples; andgenerating an analysis comparing the updated reservoir summarystatistics with the corpus summary statistics. Determining whether toadd the new data sample to the reservoir may be based on at the value ofat least one of the attributes of the new data sample. Determiningwhether to add the new data sample to the reservoir may be based on aprobabilistic sampling approach. The new data sample may be collectedfrom a data stream. The new data sample may be a single data instance ora set of data instances collected from a data stream. The new datasample may have been pre-processed by a data cleaning process.

In general, a second aspect of the subject matter described in thisspecification can be embodied in systems, methods, and computer programproducts that include the actions of receiving, from a predictive model,a judgment associated with a new data sample having a particular datatype; analyzing the new data sample based in part on the judgment and onsummary statistics associated with a reservoir of data samplesidentified based at least in part on the particular data type;determining whether to send a quality verification request including thenew data sample to an oracle; and, in an instance in which the qualityverification request is sent to the oracle, performing actions includingreceiving a data quality estimate associated with the new data samplefrom the oracle in response to the quality verification request; anddetermining whether to add the new data sample and the associatedjudgment to the reservoir of data samples in response to receiving thedata quality estimate for the new data sample from the oracle.

These and other embodiments can optionally include one or more of thefollowing features. The actions may further include updating thereservoir summary statistics in an instance in which the new data sampleand its judgment are added to the reservoir. The actions may furtherinclude comparing the reservoir summary statistics to training datasummary statistics derived from a set of training data samples havingthe particular data type, wherein the predictive model is adapted usingthe set of training data samples; and determining, based in part on thecomparing, whether to update the set of training data samples. In aninstance in which the set of training data samples is updated, theactions may include selecting at least one data sample and itsassociated judgment from the reservoir of data samples; and updating theset of training data samples using the selected data sample and itsassociated judgment. The judgment may include a confidence value.Determining whether to add the new data sample to the reservoir mayinclude determining whether the new data sample statistically belongs inthe reservoir. The data quality estimate may include a second judgmentgenerated by the oracle, and determining whether to add the new datasample and its judgment to the reservoir may include determining whetherthe judgment and the second judgment match; and, in an instance in whichthe judgment and the second judgment do not match, replacing thejudgment with the second judgment.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 illustrates an example system that can be configured to implementdynamic data quality assessment in accordance with some embodimentsdiscussed herein;

FIG. 2 is a flow diagram of an example method for automatic dynamic dataquality assessment in accordance with some embodiments discussed herein;

FIG. 3 illustrates an example adaptive data analysis system that isconfigured to include dynamic data quality assessment in accordance withsome embodiments discussed herein;

FIG. 4 is an illustration of the different effects of active learningand dynamic data quality assessment on selection of new data samples tobe added to an exemplary training data set for a binary classificationmodel in accordance with some embodiments discussed herein;

FIG. 5 is a flow diagram of an example method for automatic dynamic dataquality assessment of dynamic input data being analyzed using anadaptive predictive model in accordance with some embodiments discussedherein; and

FIG. 6 illustrates a schematic block diagram of circuitry that can beincluded in a computing device, such as a dynamic data qualityassessment device, in accordance with some embodiments discussed herein.

DETAILED DESCRIPTION

The present invention now will be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the invention are shown. Indeed, the invention may beembodied in many different forms and should not be construed as beinglimited to the embodiments set forth herein; rather, these embodimentsare provided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

As described herein, system components can be communicatively coupled toone or more of each other. Though the components are described as beingseparate or distinct, two or more of the components may be combined intoa single process or routine. The component functional descriptionsprovided herein including separation of responsibility for distinctfunctions is by way of example. Other groupings or other divisions offunctional responsibilities can be made as necessary or in accordancewith design preferences.

As used herein, the terms “data,” “content,” “information” and similarterms may be used interchangeably to refer to data capable of beingcaptured, transmitted, received, displayed and/or stored in accordancewith various example embodiments. Thus, use of any such terms should notbe taken to limit the spirit and scope of the disclosure. Further, wherea computing device is described herein to receive data from anothercomputing device, the data may be received directly from the anothercomputing device or may be received indirectly via one or moreintermediary computing devices, such as, for example, one or moreservers, relays, routers, network access points, base stations, and/orthe like. Similarly, where a computing device is described herein tosend data to another computing device, the data may be sent directly tothe another computing device or may be sent indirectly via one or moreintermediary computing devices, such as, for example, one or moreservers, relays, routers, network access points, base stations, and/orthe like.

A system that automatically identifies new businesses based on datasampled from a data stream representing data collected from a variety ofonline sources (e.g., websites, blogs, and social media) is an exampleof a system that processes dynamic data. Analysis of such dynamic datatypically is based on data-driven statistical models that depend onconsistent data quality, yet dynamic data is inherently inconsistent inits quality. The quality of the data sources may vary, the quality ofthe data collection methods may vary, and, in the case of data beingcollected continuously from a data stream, the overall quality andstatistical distribution of the data itself may vary over time. Dataquality fluctuations may affect the performance of the statisticalmodels, and, in some cases when the data quality and/or statisticaldistribution of the data has changed over time, the statistical modelsmay have to be replaced by different models that more closely fit thechanged data. Thus, it is important to be able to perform data qualityassessments on dynamic data as it is being collected, so that thequality fluctuations and statistical distribution of the data may bemonitored.

As such, and according to some example embodiments, the systems andmethods described herein are therefore configured to implement dynamicdata quality assessment. In embodiments, a system maintains one or moredata reservoirs of previously assessed data samples and their respectiveassociated quality assessments. When the system receives a new datasample, the system determines whether the new data sample statisticallybelongs in a data reservoir of data samples having the same data type asthe new data sample, and the system may determine that the new datasample should be added to the data reservoir. The system may send aquality verification request for the new data sample to a source oftruth (oracle, hereinafter) and, in response to receiving a qualityestimate of the new data sample from the oracle, may add the new datasample to the data reservoir.

In some embodiments, the system may store summary statistics for thedata reservoir; those summary statistics represent a current snapshot ofthe quality of the data samples being collected. In embodiments, thesystem may store instances of reservoir summary statistics, and may usethose summary statistics in analyses to determine quality fluctuationsand other time-based trends representative of the data being collectedand of the data sources from which the data are being collected. In someembodiments, the system may compare the reservoir summary statistics tosummary statistics calculated from a corpus of data in order todetermine overall quality of the data samples currently being collected.

In some embodiments, a dynamic data quality assessment system may be acomponent of an adaptive data analysis system that processes dynamicdata using predictive models developed using machine learningalgorithms. By repeatedly making assessments of data quality andstatistical distribution of the data samples as they are beingcollected, the dynamic data quality assessment system may providefeedback for modifying training data sets and/or feature extraction toenable incremental adaptations of predictive models to fit the dynamicdata. Incrementally adapting an existing model is less disruptive andresource-intensive than replacing the model with a new model, and alsoenables a model to evolve with the dynamic data.

FIG. 1 illustrates an example system 100 that can be configured toimplement dynamic data quality assessment according to variousembodiments of the invention. In embodiments, system 100 may comprise aquality checker 110 for assessing the quality of a particular datasample; one or more sources of truth (oracles, hereinafter) (120 a-120x) for providing a verified quality measure for a received data sample;one or more data reservoirs 130 a-130 x maintained by quality checker110, each reservoir respectively storing a group of previously assesseddata samples; and one or more persistently stored data logs accessed byquality checker 110, each log including metadata collected by thequality checker 110 during the assessment of a set of received datasamples.

In some embodiments, quality checker 110 receives a data quality job 102from a data quality job queue 105. In some embodiments, the data qualityjob 102 includes a new data sample 104 and configuration data thatincludes one or more of the type of data to be assessed (e.g., amerchant contact information record that includes the address, phonenumber, and website URL of a merchant), a method for verifying the typeof data, and an oracle identifier that indicates a particular oracle toprovide a verified quality measure for the data sample 104.

In some embodiments, a new data sample 104 may have been processed by acleaning processor prior to being included in a data quality job 102.Examples of data cleaning processing include applying one or more ofauto-normalization of the new data sample, sending the new data sampleto a crowd for completion and/or correction, geo-coding the new datasample (i.e., generating a normalized address and/or the latitude andlongitude of a location included in the new data sample), and creatingan optimized view of the new data sample by consolidating data featuresof the collected data.

In some embodiments, a data quality job 102 may be a streaming dataquality job for processing a new data sample 104 that has been collectedfrom a data stream. The streaming new data sample 104 may represent asingle data instance collected from the data stream or, alternatively,the streaming data sample 104 may represent a set of data instancescollected from the data stream within a pre-defined time window (e.g.,data collected from the data stream during a day or data collected fromthe data stream during a week).

In some embodiments, further processing of a data quality job 102 isbased at least in part on determining whether to assess the quality ofthe new data sample 104. In some embodiments, determining whether toassess the quality of the new data sample 104 is based in part ondetermining whether to add the data sample 104 to a data reservoir 130in which are stored previously assessed data samples, each stored datasample having the same data sample type as the new data sample 104.

In some embodiments, determining whether to add the new data sample 104to the data reservoir 130 is based on at the value of at least one ofthe attributes of the new data sample 104 (e.g., country of origin orlanguage if the data sample is a merchant contact information record).Additionally and/or alternatively, in some embodiments, determiningwhether to add the new data sample 104 to the data reservoir 130 isbased on a probabilistic sampling approach. For a data reservoir 130that includes a pre-defined number N of stored data samples (e.g., N=400data samples), the probability of adding the new data sample 104 is 1/Ngiven N−1 prior observed events. To maintain the constant N datasamples, adding a new data sample to a reservoir 130 includes replacinga data sample that is currently stored in the reservoir 130. Theselection of the data sample being replaced is done randomly.

In some embodiments, assessing the quality of a new data sample 104includes sending the new data sample 104 with a quality verificationrequest to an oracle 120. In some embodiments, the oracle to be sent therequest is specified in configuration data included in the data qualityjob 102. In some embodiments, quality checker 110 may be configured tosend requests to any of a group of different oracles 120 a-120 x (e.g.,a crowd, a flat file of data verification results previously receivedfrom one or more oracles, and/or data verification software). In someembodiments, the quality verification request is sent to the oracle 120asynchronously, and the oracle returns a quality estimate 106 of thedata sample 104 when processing of the quality verification requestcompletes. In some embodiments, the quality estimate 106 returned by theoracle 120 may be a quality score that is calculated based onpercentages of correctness and completeness of the data sample 104.

In some embodiments, quality checker 110 updates the data reservoir 130summary statistics in response to adding a new data sample 104 and itsassociated quality estimate 106 to the data reservoir 130. Additionallyand/or alternatively, in some embodiments, the system may update thedata reservoir 130 summary statistics after a set of new data sampleshave been added, after an external event that may affect the overallquality of the data samples (e.g., the status of a data source thatsupplies the new data samples has changed, the status of the oracleproviding the data quality estimates has changed), and/or after aparticular period of time has elapsed since the last reservoir summarystatistics update was made. The updated summary statistics arecalculated using the stored samples (and their associated qualityestimates) in the data reservoir 130, and thus represent a currentsnapshot of the quality of collected data having a particular datasample type.

In some embodiments, the updated summary statistics are added topersistently stored data logs 140. In some embodiments, the persistentlystored data logs 140 may be accessed and used in analyses fordetermining changes of data quality over time and/or making judgmentsabout overall quality of data that are being collected. In someembodiments, the system may receive summary statistics for a corpus ofdata samples, each corpus data sample having the same data sample typeas the data samples in the reservoir 130. The corpus summary statisticsmay be used as a global data quality baseline, and an analysis mayinclude comparing the summary statistics of the reservoir 130 with thesummary statistics of the corpus. Alternatively, in some embodiments,the system may access the corpus of data samples directly and calculatethe corpus summary statistics in the same way that the updated reservoirsummary statistics were calculated prior to comparing the reservoirsummary statistics and the corpus summary statistics.

FIG. 2 is a flow diagram of an example method 200 for automatic dynamicdata quality assessment. For convenience, the method 200 will bedescribed with respect to a system that includes one or more computingdevices and performs the method 200. Specifically, the method 200 willbe described with respect to processing a data quality job 102 byquality checker 110 in dynamic quality assessment system 100.

In embodiments, the system receives 205 a data quality job 102 forquality assessment processing of a new data sample 104 having aparticular data sample type. The data quality job 102 includesconfiguration data which may include one or more of the type of data tobe assessed, a method for verifying the type of data, and an oracleidentifier that indicates a particular oracle to provide a verifiedquality measure for the data sample 104. As previously described withreference to FIG. 1, the new data sample 104 may have been collectedfrom a data stream, and/or may have been pre-processed by a datacleaning process.

In embodiments, the system determines 210 whether to add the new datasample 104 to a reservoir of data samples that were previously assessedfor quality, each of the data samples having the same data sample typeas the data sample type of the new data sample 104. As previouslydescribed with reference to FIG. 1, determining whether to add the newdata sample 104 to the data reservoir is based on determining 210whether the new data sample 104 statistically belongs in the datareservoir.

In an instance in which the system determines 215 that the new datasample 104 is not to be added to the reservoir, the process ends 235.

In an instance in which the system determines 215 that the new datasample 104 is to be added to the reservoir, the system sends 220 the newdata sample 104 and a quality verification request to an oracle in orderto receive a verified data quality estimate for the new data sample 104.As previously described with reference to FIG. 1, the system may beconfigured to send quality verification requests to any of a group ofdifferent oracles 120 a-120 x, and the system chooses a particularoracle 120 from the group of oracles to receive the quality verificationrequest. In some embodiments, the choice of the particular oracle 120 toreceive the quality verification request is based on configuration dataincluded in the data quality job 102 and/or the data type of the newdata sample 102.

In some embodiments, the quality verification request is sent to theoracle 120 as an asynchronous request. In some embodiments, the oracle120 returns a pending request identifier to the system as anacknowledgement to receiving the quality verification request, and thesystem stores the new data sample and its associated pending requestidentifier until the system receives the verified data quality estimate106 result from the oracle 120. Thus, the system maintains a log of dataquality jobs in which a new data sample has been selected by the systemfor quality assessment but for which processing has been suspended toawait verification results that are returned from an oracle.

In response to receiving a data quality estimate 106 of the new datasample 104 from the oracle 120, in embodiments, the system adds 225 thenew data sample 104 and its associated data quality estimate 106 to thedata reservoir 130. As previously described with reference to FIG. 1, insome embodiments, the system may maintain the data reservoir 130 at aconstant size of N data samples. Thus, to maintain the data reservoirsize of N data samples, a currently stored data sample is removed fromthe data reservoir 130 when a new data sample 104 is added.

In embodiments, before the process ends 235, the system optionallyupdates 230 summary statistics for the reservoir 130. As previouslydescribed with reference to FIG. 1, the system may update the reservoirsummary statistics in response to adding the new data sample 104 to thereservoir and/or may update the reservoir summary statistics in responseto another internal or external event. In some embodiments, updating thereservoir summary statistics may include calculating an overall dataquality estimate for the reservoir 130 using the data quality estimatesrespectively associated with each of the data samples in the reservoirand/or calculating a statistical variance for the data samples in thereservoir 130.

In some embodiments, automated dynamic data quality assessment is usedto ensure that a predictive model for analyzing dynamic input data(e.g., data that originates from a variety of sources and is collectedcontinuously from a data stream) can adapt to the dynamic nature of thedata and thus maintain consistent and reliable predictions. For example,in embodiments, data describing information about businesses may becollected from a variety of online sources (e.g., websites, blogs), eachbusiness may be categorized (e.g., is this a restaurant?) based on thecollected data using a predictive model (e.g., a classifier), and theresults may be stored in a business search index as described, forexample, in U.S. patent application Ser. No. 13/797,570 entitled“Discovery of New Business Openings Using Web Content Analysis,” filedon Mar. 12, 2013, and which is incorporated herein in its entirety. Insome embodiments, the predictive model is a data-driven statisticalmodel that is generated from a training data set of previously processeddata samples using machine learning (e.g., the predictive model is atrainable classifier having coefficients that are adapted based on atraining data set using a supervised learning scheme, as described, forexample, in U.S. patent application Ser. No. 13/797,570).

FIG. 3 illustrates an example adaptive data analysis system 300 that isconfigured to include dynamic data quality assessment according tovarious embodiments of the invention. In embodiments, system 300 maycomprise an input data analysis module 320 for creating an optimalfeature representation (e.g., a feature vector 304) of a received inputdata sample 302; a predictive model 330 that has been generated usingmachine learning based on a set of training data 340, and that isconfigured to generate a judgment 306 about the input data sample 302 inresponse to receiving a feature vector 304 representing the input datasample 302; a quality checker 110 for assessing the quality of the inputdata sample 302 and its associated judgment 306; at least one oracle 120for providing a verified quality measure for the input data sample 302and its associated judgment 306; a data reservoir 130 maintained byquality checker 110 to store a group of input data samples and theirrespective judgments previously assessed by quality checker 110; and aquality blocker 350 that determines whether the quality of the assessedinput data sample 308 is above an acceptable quality threshold.

In some embodiments, for example, a dynamic data quality assessmentsystem, such as system 100, is used within an adaptive data analysissystem 300 to assess the quality of input data collected from a datastream, determine the effect of data quality fluctuations on theperformance of a predictive model generated from a training data set 340using machine learning, identify input data samples that currently bestrepresent examples of the modeled data, and modify the training data 340set to enable the model to be improved incrementally by being re-trainedwith a currently optimal set of examples.

In some embodiments, the predictive model generates a judgment based ona feature vector 304 that represents an optimal view of the input datasample 302 and that is generated by an input data analysis module 320.In some embodiments, the feature vector 304 is generated as a result ofstatistical analysis (e.g., cluster analysis as described, for example,in U.S. patent application Ser. No. 14/038,661 entitled “DynamicClustering for Streaming Data,” filed on Sep. 26, 2013, and which isincorporated herein in its entirety) by an input data analysis module320. In some embodiments, the dynamic data quality assessment system mayprovide feedback to an input data analysis module 320 that, due to thedynamic nature of the input data samples, the feature vectors currentlybeing generated by the input data analysis module 320 no longerrepresent optimal views of the input data samples. Less than optimalviews of the input data may affect performance of the predictive model.

In some embodiments, an adaptive data analysis system 300 may beconfigured to further include an active learning component to facilitateadaptation of the predictive model. Active learning, as described, forexample, in Settles, Burr (2009), “Active Learning Literature Survey”,Computer Sciences Technical Report 1648. University ofWisconsin-Madison, is semi-supervised learning, in which thedistribution of samples composing a training data set can be adjusted tooptimally represent a machine learning problem by interactively queryinga source of truth (e.g., an oracle) to assign labels to new data samplesthat are to be added to the training data set. In embodiments, a dynamicdata quality assessment system may complement an active learningcomponent to ensure that any modifications of the training data byadding new samples to the training data set do not result inover-fitting the model to the problem.

FIG. 4 is an illustration of the different effects of active learningand dynamic data quality assessment on selection of new data samples tobe added to an exemplary training data set for a binary classificationmodel. A model (i.e., a classifier) assigns a judgment value 410 to eachdata point; a data point assigned a judgment value that is close toeither 0 or 1 has been determined with certainty by the classifier tobelong to one or the other of the classes. A judgment value of 0.5represents a situation in which the classification decision was notcertain; an input data sample assigned a judgment value close to 0.5 bythe classifier represents a judgment that is close to the decisionboundary 415 for the classification task.

The dashed curve 440 represents the relative frequencies of new trainingdata samples that would be added to a training data set for this binaryclassification problem by an active learning component. To enhance theperformance of the classifier in situations where the decision wasuncertain, the active learning component would choose the majority ofnew training data samples from input data that resulted in decisionsnear the decision boundary 415.

The solid curve 430 represents the relative frequencies of new trainingdata samples that would be added to the training data set by dynamicquality assessment. Instead of choosing new training data samples basedon the judgment value, in some embodiments, dynamic quality assessmentmay choose the majority of new training data samples based on whetherthey statistically belong in the reservoir. It also may choose to addnew training data samples that were classified with certainty (i.e.,having a judgment value close to either 0 or 1), but erroneously (e.g.,samples in which the judgment result from the classifier did not matchthe result returned from the oracle).

FIG. 5 is a flow diagram of an example method 500 for automatic dynamicdata quality assessment of dynamic input data being analyzed using anadaptive predictive model. For convenience, the method 500 will bedescribed with respect to a system that includes one or more computingdevices and performs the method 500. Specifically, the method 500 willbe described with respect to processing an input data sample 302 and itsassociated judgment 306 from a predictive model 330 by quality checker110 in adaptive data analysis system 300.

For clarity and without limitation, method 500 will be described for ascenario in which the input data sample 302 is a sample of datacollected from a data stream, and in which the predictive model 330 is atrainable classifier, adapted based on a set of training data 340. Theclassifier 330 is configured to receive a feature vector 304representing a view of the input data sample 302 and to output ajudgment 306 about the input data sample 302.

In embodiments, the system receives 505 a judgment 306 about an inputdata sample 302 from a classifier. In some embodiments, the judgmentincludes a confidence value that represents a certainty of the judgment306. For example, in some embodiments, the confidence value may be ascore that represents the distance of the judgment from the decisionboundary in decision space for the particular classification problemmodeled by the classifier. The confidence score is higher (i.e., thejudgment is more certain) for judgments that are further from thedecision boundary.

As previously described with reference to FIG. 1, in some embodiments,the system maintains a data reservoir 130 of data samples that have thesame data type as the input data sample 302 and that have been processedpreviously by the classifier 330. In embodiments, the system analyzes510 the input data sample 302 in terms of the summary statistics of thedata reservoir and/or the judgment 306. In some embodiments, analysis ofthe judgment 306 may include comparing a confidence value associatedwith the judgment to a confidence threshold and/or determining whetherthe judgment 306 matches a judgment determined previously for the inputsample by a method other than the classifier.

In embodiments, the system determines 515 whether to send a qualityverification request for the input data sample to an oracle 120 based onthe analysis. For example, in some embodiments, the system may determineto send a quality verification request for the input data sample if thedata sample is determined statistically to be an outlier to the datasamples in the data reservoir. In another example, the system maydetermine to send a quality verification request for the input datasample if the judgment is associated with a confidence value that isbelow a confidence threshold. In a third example, the system maydetermine to send a quality verification request for the input datasample if the judgment generated by the classifier does not match ajudgment generated by another method, even if the confidence valueassociated with the classifier's judgment is above the confidencethreshold.

In an instance in which the system determines 520 that a quality requestwill not be sent to the oracle, the process ends 540.

In an instance in which the system determines 520 that a quality requestwill be sent to the oracle, in some embodiments, as previously describedwith reference to FIG. 1, the system may be configured to send requeststo any of a group of different oracles (e.g., a crowd, a flat file ofdata verification results previously received from one or more oracles,and/or data verification software) and the system may select the oracleto receive the quality verification request based on attributes of theinput data sample 302.

In response to receiving a data quality estimate of the input datasample 302 from the oracle 120, in embodiments, the system determines525 whether to add the input data sample, its associated judgment, andits data quality estimate to the data reservoir. In some embodiments,the determination may be based on whether the input data sample 302statistically belongs in the data reservoir, as described previouslywith reference to FIG. 2. Additionally and/or alternatively, thedetermination may be based on whether the judgment 306 is associatedwith a high confidence value and/or matches a judgment made by a methoddifferent from the classifier (e.g., the oracle).

In an instance in which the system determines 525 that the new datasample 302 is not to be added to the reservoir, the process ends 540.

In an instance in which the system determines 525 that the new datasample 302 is to be added to the reservoir, before the process ends 540,the system optionally updates 230 summary statistics for the reservoir130 as previously described with reference to FIG. 2.

FIG. 6 shows a schematic block diagram of circuitry 600, some or all ofwhich may be included in, for example, dynamic data quality assessmentsystem 100. As illustrated in FIG. 6, in accordance with some exampleembodiments, circuitry 600 can include various means, such as processor602, memory 604, communications module 606, and/or input/output module608. As referred to herein, “module” includes hardware, software and/orfirmware configured to perform one or more particular functions. In thisregard, the means of circuitry 600 as described herein may be embodiedas, for example, circuitry, hardware elements (e.g., a suitablyprogrammed processor, combinational logic circuit, and/or the like), acomputer program product comprising computer-readable programinstructions stored on a non-transitory computer-readable medium (e.g.,memory 604) that is executable by a suitably configured processingdevice (e.g., processor 602), or some combination thereof.

Processor 602 may, for example, be embodied as various means includingone or more microprocessors with accompanying digital signalprocessor(s), one or more processor(s) without an accompanying digitalsignal processor, one or more coprocessors, one or more multi-coreprocessors, one or more controllers, processing circuitry, one or morecomputers, various other processing elements including integratedcircuits such as, for example, an ASIC (application specific integratedcircuit) or FPGA (field programmable gate array), or some combinationthereof. Accordingly, although illustrated in FIG. 6 as a singleprocessor, in some embodiments processor 602 comprises a plurality ofprocessors. The plurality of processors may be embodied on a singlecomputing device or may be distributed across a plurality of computingdevices collectively configured to function as circuitry 600. Theplurality of processors may be in operative communication with eachother and may be collectively configured to perform one or morefunctionalities of circuitry 600 as described herein. In an exampleembodiment, processor 602 is configured to execute instructions storedin memory 604 or otherwise accessible to processor 602. Theseinstructions, when executed by processor 602, may cause circuitry 600 toperform one or more of the functionalities of circuitry 600 as describedherein.

Whether configured by hardware, firmware/software methods, or by acombination thereof, processor 602 may comprise an entity capable ofperforming operations according to embodiments of the present inventionwhile configured accordingly. Thus, for example, when processor 602 isembodied as an ASIC, FPGA or the like, processor 602 may comprisespecifically configured hardware for conducting one or more operationsdescribed herein. Alternatively, as another example, when processor 602is embodied as an executor of instructions, such as may be stored inmemory 604, the instructions may specifically configure processor 602 toperform one or more algorithms and operations described herein, such asthose discussed in connection with FIGS. 1-2, FIG. 3, and FIG. 5.

Memory 604 may comprise, for example, volatile memory, non-volatilememory, or some combination thereof. Although illustrated in FIG. 6 as asingle memory, memory 604 may comprise a plurality of memory components.The plurality of memory components may be embodied on a single computingdevice or distributed across a plurality of computing devices. Invarious embodiments, memory 604 may comprise, for example, a hard disk,random access memory, cache memory, flash memory, a compact disc readonly memory (CD-ROM), digital versatile disc read only memory (DVD-ROM),an optical disc, circuitry configured to store information, or somecombination thereof. Memory 604 may be configured to store information,data (including analytics data), applications, instructions, or the likefor enabling circuitry 600 to carry out various functions in accordancewith example embodiments of the present invention. For example, in atleast some embodiments, memory 604 is configured to buffer input datafor processing by processor 602. Additionally or alternatively, in atleast some embodiments, memory 604 is configured to store programinstructions for execution by processor 602. Memory 604 may storeinformation in the form of static and/or dynamic information. Thisstored information may be stored and/or used by circuitry 600 during thecourse of performing its functionalities.

Communications module 606 may be embodied as any device or meansembodied in circuitry, hardware, a computer program product comprisingcomputer readable program instructions stored on a computer readablemedium (e.g., memory 604) and executed by a processing device (e.g.,processor 602), or a combination thereof that is configured to receiveand/or transmit data from/to another device, such as, for example, asecond circuitry 600 and/or the like. In some embodiments,communications module 606 (like other components discussed herein) canbe at least partially embodied as or otherwise controlled by processor602. In this regard, communications module 606 may be in communicationwith processor 602, such as via a bus. Communications module 606 mayinclude, for example, an antenna, a transmitter, a receiver, atransceiver, network interface card and/or supporting hardware and/orfirmware/software for enabling communications with another computingdevice. Communications module 606 may be configured to receive and/ortransmit any data that may be stored by memory 604 using any protocolthat may be used for communications between computing devices.Communications module 606 may additionally or alternatively be incommunication with the memory 604, input/output module 608 and/or anyother component of circuitry 600, such as via a bus.

Input/output module 608 may be in communication with processor 602 toreceive an indication of a user input and/or to provide an audible,visual, mechanical, or other output to a user. Some example visualoutputs that may be provided to a user by circuitry 600 are discussed inconnection with FIG. 1 and FIG. 3. As such, input/output module 608 mayinclude support, for example, for a keyboard, a mouse, a joystick, adisplay, a touch screen display, a microphone, a speaker, a RFID reader,barcode reader, biometric scanner, and/or other input/output mechanisms.In embodiments wherein circuitry 600 is embodied as a server ordatabase, aspects of input/output module 608 may be reduced as comparedto embodiments where circuitry 600 is implemented as an end-user machineor other type of device designed for complex user interactions. In someembodiments (like other components discussed herein), input/outputmodule 608 may even be eliminated from circuitry 600. Alternatively,such as in embodiments wherein circuitry 600 is embodied as a server ordatabase, at least some aspects of input/output module 608 may beembodied on an apparatus used by a user that is in communication withcircuitry 600, such as for example, pharmacy terminal 108. Input/outputmodule 608 may be in communication with the memory 604, communicationsmodule 606, and/or any other component(s), such as via a bus. Althoughmore than one input/output module and/or other component can be includedin circuitry 600, only one is shown in FIG. 6 to avoid overcomplicatingthe drawing (like the other components discussed herein).

Quality checker module 610 may also or instead be included andconfigured to perform the functionality discussed herein related to thedynamic data quality assessment discussed above. In some embodiments,some or all of the functionality of dynamic data quality assessment maybe performed by processor 602. In this regard, the example processes andalgorithms discussed herein can be performed by at least one processor602 and/or quality checker module 610. For example, non-transitorycomputer readable media can be configured to store firmware, one or moreapplication programs, and/or other software, which include instructionsand other computer-readable program code portions that can be executedto control each processor (e.g., processor 602 and/or quality checkermodule 610) of the components of system 400 to implement variousoperations, including the examples shown above. As such, a series ofcomputer-readable program code portions are embodied in one or morecomputer program products and can be used, with a computing device,server, and/or other programmable apparatus, to producemachine-implemented processes.

Any such computer program instructions and/or other type of code may beloaded onto a computer, processor or other programmable apparatus'scircuitry to produce a machine, such that the computer, processor otherprogrammable circuitry that execute the code on the machine create themeans for implementing various functions, including those describedherein.

It is also noted that all or some of the information presented by theexample displays discussed herein can be based on data that is received,generated and/or maintained by one or more components of dynamic dataquality assessment system 100. In some embodiments, one or more externalsystems (such as a remote cloud computing and/or data storage system)may also be leveraged to provide at least some of the functionalitydiscussed herein.

As described above in this disclosure, aspects of embodiments of thepresent invention may be configured as methods, mobile devices, backendnetwork devices, and the like. Accordingly, embodiments may comprisevarious means including entirely of hardware or any combination ofsoftware and hardware. Furthermore, embodiments may take the form of acomputer program product on at least one non-transitorycomputer-readable storage medium having computer-readable programinstructions (e.g., computer software) embodied in the storage medium.Any suitable computer-readable storage medium may be utilized includingnon-transitory hard disks, CD-ROMs, flash memory, optical storagedevices, or magnetic storage devices.

Embodiments of the present invention have been described above withreference to block diagrams and flowchart illustrations of methods,apparatuses, systems and computer program products. It will beunderstood that each block of the circuit diagrams and process flowdiagrams, and combinations of blocks in the circuit diagrams and processflowcharts, respectively, can be implemented by various means includingcomputer program instructions. These computer program instructions maybe loaded onto a general purpose computer, special purpose computer, orother programmable data processing apparatus, such as processor 602and/or quality checker module 610 discussed above with reference to FIG.6, to produce a machine, such that the computer program product includesthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable storage device (e.g., memory 604) that can direct acomputer or other programmable data processing apparatus to function ina particular manner, such that the instructions stored in thecomputer-readable storage device produce an article of manufactureincluding computer-readable instructions for implementing the functiondiscussed herein. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions discussed herein.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the circuit diagrams and processflowcharts, and combinations of blocks in the circuit diagrams andprocess flowcharts, can be implemented by special purpose hardware-basedcomputer systems that perform the specified functions or steps, orcombinations of special purpose hardware and computer instructions

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1-24. (canceled)
 25. An apparatus for automated adaptive data analysis,the apparatus comprising at least one processor and at least one memorystoring instructions that, with the at least one processor, cause theapparatus to: generate, using a predictive model, a judgment associatedwith a new data sample received in a data stream, the new data samplecomprising a data type, wherein the new data sample is collected from adata stream, wherein the judgment is generated based on a feature vectorassociated with the new data sample, and wherein the feature vectorrepresents an optimal view of the new data sample; and update reservoirsummary statistics associated with a reservoir of data samples inresponse to determining whether to add the new data sample and thejudgment to the reservoir of data samples, wherein the determining isbased on one of whether the new data sample statistically belongs in thedata reservoir or whether the judgment is associated with a highconfidence value.
 26. The apparatus of claim 25, wherein the determiningis in response to receiving a data quality estimate from an oracle. 27.The apparatus of claim 25, wherein the reservoir of data samples isidentified based at least in part on the data type.
 28. The apparatus ofclaim 25, wherein the predictive model is adapted using a set oftraining data samples having the data type.
 29. The apparatus of claim28, wherein the at least one memory storing instructions, with the atleast one processor, further cause the apparatus to: update the set oftraining data samples in response to comparing the reservoir summarystatistics to training data summary statistics derived from the set oftraining data samples having the data type and determining to update theset of training data samples.
 30. The apparatus of claim 25, wherein theat least one memory storing instructions that, with the at least oneprocessor, cause the apparatus to: generate, using the predictive model,a confidence value associated with the judgment.
 31. The apparatus ofclaim 25, wherein the at least one memory storing instructions that,with the at least one processor, cause the apparatus to: replace thejudgment with a second judgment associated with the new data samplereceived from the oracle when the judgment and second judgment do notmatch.
 32. The apparatus of claim 25, wherein the at least one memorystoring instructions that, with the at least one processor, cause theapparatus to: sent a quality verification request comprising the newdata sample to the oracle.
 33. A computer readable storage mediumcomprising a non-transitory storage medium for storing instructionsthat, when executed by at least one processor, cause the processor to:generate, using a predictive model, a judgment associated with a newdata sample received in a data stream, the new data sample comprising adata type, wherein the new data sample is collected from a data stream,wherein the judgment is generated based on a feature vector associatedwith the new data sample, and wherein the feature vector represents anoptimal view of the new data sample; and update reservoir summarystatistics associated with a reservoir of data samples in response todetermining whether to add the new data sample and the judgment to thereservoir of data samples, wherein the determining is based on one ofwhether the new data sample statistically belongs in the data reservoiror whether the judgment is associated with a high confidence value. 34.The computer readable storage medium of claim 33, wherein thedetermining is in response to receiving a data quality estimate from anoracle.
 35. The computer readable storage medium of claim 33, whereinthe reservoir of data samples is identified based at least in part onthe data type.
 36. The computer readable storage medium of claim 33,wherein the predictive model is adapted using a set of training datasamples having the data type.
 37. The computer readable storage mediumof claim 36, comprising a non-transitory storage medium for storinginstructions that, when executed by at least one processor, furthercause the processor to: update the set of training data samples inresponse to comparing the reservoir summary statistics to training datasummary statistics derived from the set of training data samples havingthe data type and determining to update the set of training datasamples.
 38. The computer readable storage medium of claim 33,comprising a non-transitory storage medium for storing instructionsthat, when executed by at least one processor, further cause theprocessor to: generate, using the predictive model, a confidence valueassociated with the judgment.
 39. The computer readable storage mediumof claim 33, comprising a non-transitory storage medium for storinginstructions that, when executed by at least one processor, furthercause the processor to: replace the judgment with a second judgmentassociated with the new data sample received from the oracle when thejudgment and second judgment do not match.
 40. The computer readablestorage medium of claim 33, wherein the at least one memory storinginstructions that, with the at least one processor, cause the apparatusto: sent a quality verification request comprising the new data sampleto the oracle.
 41. A computer implemented method, comprising:generating, using at least one processor and a predictive model, ajudgment associated with a new data sample received in a data stream,the new data sample comprising a data type, wherein the new data sampleis collected from a data stream, wherein the judgment is generated basedon a feature vector associated with the new data sample, and wherein thefeature vector represents an optimal view of the new data sample; andupdating, using the at least one processor, reservoir summary statisticsassociated with a reservoir of data samples in response to determiningwhether to add the new data sample and the judgment to the reservoir ofdata samples, wherein the determining is based on one of whether the newdata sample statistically belongs in the data reservoir or whether thejudgment is associated with a high confidence value.
 42. The method ofclaim 41, wherein the determining is in response to receiving a dataquality estimate from an oracle.
 43. The method of claim 41, wherein thereservoir of data samples is identified based at least in part on thedata type.
 44. The method of claim 41, wherein the predictive model isadapted using a set of training data samples having the data type.